A content-based recommender system with consideration of repeat purchase behavior

Abstract

With the increasing popularity of online shopping and information explosion, personalized recommender systems for e-commerce become more and more necessary, which helps customers find the desired products efficiently among variety of categories based on their previous behavior such as buying pattern and rating history. However, most recommender systems for e-commerce adopt binary (purchase/non-purchase) or subjective weighting methods to represent the customer preferences, which is hard to predict their profiles precisely since rapid change in tastes. Therefore, this study focuses on the application of transactional data. A personalized recommender system for e-commerce (PROSE) is proposed in order to enhance the quality of recommendations by integrating the architecture of traditional content-based recommender system with a new component called feedback adjuster, which is designed to make customer implicit feedback reflects the reality of preferences as possible through taking into consideration their behavior of repeat purchase. The computational results indicate that the proposed algorithm is able to outperform other algorithms.